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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/57386

    Título
    Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment
    Autor
    Ruiz Gómez, Saúl JoséAutoridad UVA
    Gómez Peña, CarlosAutoridad UVA Orcid
    Poza Crespo, JesúsAutoridad UVA Orcid
    Gutierrez Tobal, Gonzalo CésarAutoridad UVA Orcid
    Tola Arribas, Miguel ÁngelAutoridad UVA Orcid
    Cano, Mónica
    Hornero Sánchez, RobertoAutoridad UVA Orcid
    Año del Documento
    2018
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Entropy, 2018, vol. 20, n. 1, p. 35
    Zusammenfassung
    The discrimination of early Alzheimer’s disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel–Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.
    Materias Unesco
    12 Matemáticas
    32 Ciencias Médicas
    Palabras Clave
    Alzheimer’s disease
    Mild cognitive impairment
    Electroencephalography (EEG)
    Spectral analysis
    Nonlinear analysis
    Multiclass classification approach
    Revisión por pares
    SI
    DOI
    10.3390/e20010035
    Patrocinador
    Ministerio de Economía y Competitividad y “Fondo Europeo de Desarrollo Regional” (FEDER) proyecto “Análisis y obtención del genoma entre el completo y la actividad cerebral para la ayuda en el diagnóstico de la enfermedad de Alzheimer” (“Programa de Cooperación Interreg V-A España-Portugal, POCTEP 2014–2020”),(underl proyect TEC2014-53196-R)
    Junta de Castilla y León - Consejería de Educación y FEDER en el marco del proyecto VA037U16.
    Version del Editor
    https://www.mdpi.com/1099-4300/20/1/35
    Propietario de los Derechos
    © 2018 The Author(s)
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/57386
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • IMUVA - Artículos de Revista [103]
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    Dateien zu dieser Ressource
    Nombre:
    Automated-Multiclass-classification.pdf
    Tamaño:
    1.207Mb
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